Journal of Science Education and Technology

, Volume 26, Issue 2, pp 207–222 | Cite as

The Impact of Design-Based STEM Integration Curricula on Student Achievement in Engineering, Science, and Mathematics

  • S. Selcen Guzey
  • Michael Harwell
  • Mario Moreno
  • Yadira Peralta
  • Tamara J. Moore
Article

Abstract

The new science education reform documents call for integration of engineering into K-12 science classes. Engineering design and practices are new to most science teachers, meaning that implementing effective engineering instruction is likely to be challenging. This quasi-experimental study explored the influence of teacher-developed, engineering design-based science curriculum units on learning and achievement among grade 4–8 students of different races, gender, special education status, and limited English proficiency (LEP) status. Treatment and control students (n = 4450) completed pretest and posttest assessments in science, engineering, and mathematics as well as a state-mandated mathematics test. Single-level regression results for science outcomes favored the treatment for one science assessment (physical science, heat transfer), but multilevel analyses showed no significant treatment effect. We also found that engineering integration had different effects across race and gender and that teacher gender can reduce or exacerbate the gap in engineering achievement for student subgroups depending on the outcome. Other teacher factors such as the quality of engineering-focused science units and engineering instruction were predictive of student achievement in engineering. Implications for practice are discussed.

Keywords

Engineering curriculum Engineering integration STEM Student learning 

References

  1. American Educational Research Association, American Psychological Association, & National Council on Measurement in Education (1999) Standards for educational and psychological testing. American Educational Research Association, Washington, DCGoogle Scholar
  2. Apedoe XS, Reynolds B, Ellefson MR, Schunn CD (2008) Bringing engineering design into high school science classrooms: the heating/cooling unit. J Sci Educ Technol 17(5):454–465CrossRefGoogle Scholar
  3. Azevedo FS, Martalock PL, Keser T (2015) The discourse of design-based science classroom activities. Cult Stud Sci Educ 10(2):285–315CrossRefGoogle Scholar
  4. Berland, L., Steingut, R., & Ko, P. (2014). High school student perceptions of the utility of the engineering design process: creating opportunities to engage in engineering practices and apply math and science content. Journal of Science Education and Technology, 705–720.Google Scholar
  5. Brophy S, Klein S, Portsmore M, Rogers C (2008) Advancing engineering education in K-12 classrooms. J Eng Educ:369–387Google Scholar
  6. Brown JS, Collins A, Duguid P (1989) Situated cognition and the culture of learning. Educ Res 18(1):32–42CrossRefGoogle Scholar
  7. Bryk AS, Raudenbush SW, Congdon R (2011) Hierarchical linear and nonlinear modeling [computer software manual]. Scientific Software International, Lincolnwood, ILGoogle Scholar
  8. Cobb P, Bowers J (1999) Cognitive and situated learning perspective in theory and practice. Educ Res 28(2):4–15CrossRefGoogle Scholar
  9. Cantrell P, Pekcan G, Itani A, Velasquez-Bryant N (2006) The effects of engineering modules on student learning in middle school science classrooms. J Eng Educ 95:301–309CrossRefGoogle Scholar
  10. Carlson LE, Sullivan JF (2004) Exploiting design to inspire interest in engineering across K-16 curriculum. Int J Eng Educ 20(3):372–380Google Scholar
  11. Dare E, Ellis J, Roehrig GH (2014) Driven by beliefs: understanding challenges physical science teachers face when integrating engineering and physics. J Pre-College Eng Educ Res 4(2)Google Scholar
  12. Dehejia RH, Wahba S (2002) Propensity score-matching methods for nonexperimental causal studies. Revi Econ Stat 84(1):151–161CrossRefGoogle Scholar
  13. Desimone LM (2011) A primer on effective professional development. Phi Delta Kappan 92(6):68–71CrossRefGoogle Scholar
  14. Desimone LM, Smith TM, Phillips KJR (2013) Linking student achievement growth to professional development participation and changes in instruction: a longitudinal study of elementary students and teachers in Title I schools. Teach Coll Rec 115:1–46Google Scholar
  15. Desimone L, Garet M (2015) Best practices in teachers’ professional development in the United States. Psychol Soc Educ 7(3):252–263Google Scholar
  16. Gelman A, Hill J (2007) Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, Cambridge, U.K.Google Scholar
  17. Garet MS, Porter AC, Desimone L, Birman BF, Yoon KS (2001) What makes professional development effective? Results from a national sample of teachers. Am Educ Res J 38(4):915–945CrossRefGoogle Scholar
  18. Guo XS, Rosenbaum PR (1993) Comparison of multivariate matching methods: structures, distances, and algorithms. J Comput Graph Stat 2:405–420Google Scholar
  19. Guzey SS, Moore T, Harwell M (2014) Development of an instrument to measure students’ attitudes toward STEM. Sch Sci Math 114(6):271–279Google Scholar
  20. Guzey SS, Moore T, Harwell M (2016) Building up STEM: an analysis of teacher-developed engineering design-based STEM integration curricular materials. Journal of Pre-College Engineering Education Research (JPEER) 6(1). doi: 10.7771/2157-9288.1129
  21. Harwell M, Philips A, Mareno M, Guzey SS, Moore T (2015) A study of STEM assessments in Engineering, Science, and Mathematics Assessments for elementary and middle school students. Sch Sci Math 115(2):66–74Google Scholar
  22. Hedges LV, Hedberg EC (2007) Intraclass correlation values for planning group-randomized trials in education. Educ Eval Policy Anal 29:60–87CrossRefGoogle Scholar
  23. Kolen MJ, Brennan RL (2004) Test equating, scaling, and linking. Springer, New York, NYCrossRefGoogle Scholar
  24. Lachapelle C, Cunningham C (2014) Engineering in elementary schools. In: Purzer S, Strobel J, Cardella M (eds) Engineering in pre-college settings: synthesizing research, policy, and practices. Purdue University Press, West Lafayette: IN, pp. 61–88Google Scholar
  25. Lachapelle CP, Cunningham CM, Jocz J, Kay AE, Phadnis P, Wertheimer J, Arteaga R (2011) Engineering is elementary: an evaluation of years 4 through 6 field testing. Museum of Science, Boston, MAGoogle Scholar
  26. Little RJA, Rubin DB (2002) Statistical analysis with missing data (2nd Ed). Wiley, New York, NYGoogle Scholar
  27. Ludlow LH, Haley SM (1995) Rasch model logits: interpretation, use, and transformation. Educ Psychol Meas 55:967–975CrossRefGoogle Scholar
  28. Mehalik MM, Doppelt Y, Schunn CD (2008) Middle-school science through design-based learning versus scripted inquiry: better overall science concept learning and equity gap reduction. J Eng Educ 97(71–85)Google Scholar
  29. Moore TJ, Stohlmann MS, Wang H, Tank KM, Glancy AW, Roehrig GH (2014) Implementation and integration of engineering in K-12 STEM education. In: Purzer S, Strobel J, Cardella M (eds) Engineering in pre-college settings: Research into practice. Purdue University Press, West Lafayette, pp 35–60Google Scholar
  30. National Academy of Engineering and National Research Council (2014) STEM integration in K-12 education: status, prospects, and an agenda for research. The National Academies Press, Washington, DCGoogle Scholar
  31. National Research Council (2009) Engineering in K-12 education: understanding the status and improving the prospects. The National Academies Press, Washington, DCGoogle Scholar
  32. National Research Council (2010) Standards for K-12 engineering education? The National Academies Press, Washington, DCGoogle Scholar
  33. National Research Council (2011) Successful K-12 STEM education: identifying effective approaches in science, technology, engineering, and mathematics. National Academies Press, Washington, DCGoogle Scholar
  34. National Research Council (2012) A framework for K–12 science education. Retrieved from www.nap.edu/catalog.php?record_id=13165
  35. Nelson T, Lesseig K, Slavit D, Kennedy C, Seidel R (2015) Supporting middle school teachers implementation of STEM design challenges. Paper presented at NARST conference. IL, ChicagoGoogle Scholar
  36. Neter J, Kutner MH, Nachtsheim CJ, Wasserman W (1996) Applied linear statistical models (4th ed.). Irwin, Chicago, ILGoogle Scholar
  37. NGSS Lead States (2013) Next generation science standards: for states, by states. The National Academic Press, Washington, DCGoogle Scholar
  38. Oh Y, Lachapelle C, Shams M, Hertel J, Cunnigham C (2016) Evaluating the efficacy of engineering is elementary for student learning of engineering and science concepts. Poster presented at the American Educatonal Research Association Annual Meeting. Washington, DC Retrieved from http://www.eie.org/sites/default/files/research_article/research_file/aera_oh_evaluating_the_efficacy_poster.pdf Google Scholar
  39. Raudenbush SW, Bryk AS (2002) Hierarchical linear models: applications and data analysis methods (2nd Ed). Sage, Newbury Park, CAGoogle Scholar
  40. Riskowski JL, Todd CD, Wee B, Dark M, Harbor J (2009) Exploring the effectiveness of an interdisciplinary water resources engineering module in an eighth grade science course. Int J Eng Educ 25(1):181–195Google Scholar
  41. Sawada D, Piburn MD, Judson E, Turley J, Falconer K, Benford R, Bloom I (2002) Measuring reform practices in science and mathematics classrooms: the reformed teaching observation protocol. Sch Sci Math 102(6):245–253CrossRefGoogle Scholar
  42. Schnittka CG, Bell RL (2011) Engineering design and conceptual change in the middle school science classroom. Int J Sci Educ 33:1861–1887CrossRefGoogle Scholar
  43. Sekhon JS (2011) Multivariate and propensity score matching software with automated balance optimization: the matching package for R. J Stat Softw 42(7):1–52CrossRefGoogle Scholar
  44. Shadish WR, Cook TD, Campbell DT (2002) Experimental and quasi-experimental designs for generalized causal inference. Houghton-Mifflin, BostonGoogle Scholar
  45. Spybrook, L., Bloom, H., Congdon, R., Hill, C., Martinez, A., & Raudenbush, S. (2011). Optimal design plus empirical evidence: documentation for the “Optimal Design” software (version 3.0) [computer software]. Retrieved from http://sitemaker.umich.edu/group-based
  46. Tran NA, Nathan MJ (2010) Pre-college engineering studies: an investigation of the relationship between pre-college engineering studies and student achievement in science and mathematics. J Eng Educ 99(2):143–157CrossRefGoogle Scholar
  47. U. S. Department of Education. (2010). Digest of education statistics. Retrieved from http://nces.ed.gov/programs/digest/d09/tables/dt09_066.asp
  48. U. S. Department of Education. (2014a). Digest of education statistics. Retrieved from http://nces.ed.gov/programs/digest/d13/tables/dt13_221.40.asp
  49. U. S. Department of Education. (2014b). Digest of education statistics. Retrieved from http://nces.ed.gov/programs/digest/d13/tables/dt13_222.50.asp
  50. U. S. Department of Education. (2014c). Digest of education statistics. Retrieved from http://nces.ed.gov/programs/digest/d13/tables/dt13_102.40.asp
  51. U. S. Department of Education. (2014d). Public high school four-year on-time graduation rates and event dropout rates: school years 2010–11 and 2011–12. Retrieved from http://nces.ed.gov/pubs2014/2014391.pdf
  52. U. S. Bureau of the Census. (2014). Public education finances 2012. Retrieved from http://www2.census.gov/govs/school/12f33pub.pdf
  53. Valtorta CG, Berland LK (2015) Math, science, and engineering integration in a high school engineering course: a qualitative study. J Pre-College Eng Educ 5(1):15–29Google Scholar
  54. Wang HH, Moore T, Roehrig G, Park MS (2011) STEM integration: teacher perceptions and practice. Journal of Pre-College Engineering Education Research (J-PEER) 1(2). doi: 10.5703/1288284314636
  55. Wendell K, Rogers C (2013) Engineering design-based science, science content performance, and science attitudes in elementary school. J Eng Educ 102(4):513–540CrossRefGoogle Scholar
  56. What Works Clearinghouse (2014). Procedures and standards handbook. Retrieved from http://ies.ed.gov/ncee/wwc/
  57. Wilson SM (2013) Professional development for science teachers. Science 340:310–313CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • S. Selcen Guzey
    • 1
  • Michael Harwell
    • 2
  • Mario Moreno
    • 2
  • Yadira Peralta
    • 2
  • Tamara J. Moore
    • 3
  1. 1.Department of Curriculum and Instruction and Department of Biological SciencesPurdue UniversityWest LafayetteUSA
  2. 2.Department of Educational PsychologyUniversity of MinnesotaMinneapolisUSA
  3. 3.School of Engineering EducationPurdue UniversityWest LafayetteUSA

Personalised recommendations